Processes for Implementing Community Health Worker Workforce Development Initiatives
Bibliographic record
Abstract
Introduction: The objective of this observational, cross-sectional study was to identify, document, and assess the progress made to date in implementing various processes involved in statewide community health worker (CHW) workforce development initiatives. Methods: From September 2017 to December 2020, we developed and applied a conceptual model of processes involved in implementing statewide CHW initiatives. One or more outputs were identified for each model process and assessed across the 50 states, D.C., and Puerto Rico using peer-reviewed and gray literature available as of September 2020. Results: Twelve statewide CHW workforce development processes were identified, and 21 outputs were assessed. We found an average of eight processes implemented per state, with seven states implementing all 12 processes. As of September 2020, 45 states had a multi-stakeholder CHW coalition and 31 states had a statewide CHW organization. In 20 states CHWs were included in Medicaid Managed Care Organizations or Health Plans. We found routine monitoring of statewide CHW employment in six states. Discussion: Stakeholders have advanced statewide CHW workforce development initiatives using the processes reflected in our conceptual model. Our results could help to inform future CHW initiative design, measurement, monitoring, and evaluation efforts, especially at the state level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".